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Information & Management

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A comprehensive framework of information system design to provideorganizational creativity support

Celina M. Olszak⁎, Tomasz Bartuś, Paweł LorekUniversity of Economics in Katowice, Poland

A R T I C L E I N F O

Keywords:Organizational creativityDesign of organizational creativity supportsystemResource-based viewIntelligent agents

A B S T R A C T

This research is motivated by two considerations: (1) organizational creativity is a component that enhances theability of organizations to retain their competitive advantage and (2) too little research has been conductedworldwide that focuses on the design of information systems to provide organizational creativity support. Thisresearch proposes a comprehensive and conceptual framework for the design of organizational creativity supportsystems. To address the objective of this study, two theories, the resource-based view and a multiagent approach,are used to build the model proposed. The customer opinions from websites concerning a consumer electronicsproduct are used to validate such a system. The theoretical contributions, practical implications, and futuredirections of the study are presented and discussed.

1. Introduction

Organizational creativity is considered one of the most activelydeveloping research areas. It is asserted that it is a main vehicle oforganizational development, the basis for staying on the market andinnovative success [1–4]. Organizations face the need to constantlygenerate new and useful ideas that concern products, services, pro-cesses, managerial practices, and competitive strategies. They arerequired to have a strategic organization's capability, meaning adaptingto changing environmental conditions through continuous acquisitionof new information resources and the creation of new configurationsfrom them [5–7]. Effective support of acquiring, collecting, storing, andanalyzing different information resources and discovering new knowl-edge and rapidly disseminating it are of crucial importance.

Several arguments can be found in the pertinent literature that ITenables organizations faster and easier access to information, improv-ing creativity in business processes and better communication betweenemployees and all stakeholders [8]. IT enables an organization tosearch and absorb new knowledge that is needed in organizationalcreativity and solving business problems. On the other hand, thepractice shows that success from IT-based creativity support is stillquestionable. Organizations do not achieve the appropriate benefitsfrom IT usage. Many organizations are not able to make IT an effectivetool for creativity support. The reasons for this failure are not clear andstill not well investigated. Therefore, the need for a more systematicand deliberate study of information system design to support organiza-tional creativity is crucial.

Although there have been numerous studies on creativity over thepast three decades, they have not addressed the essence of organiza-tional creativity support systems (OCSSs) and their design. They havebeen mainly focused on creative problem solving, creative processes,and individual creativity support. The issue of the design of informationsystems for organizational creativity support is still insufficientlyinvestigated. The research studies are fragmentary and scattered.There is a lack of a comprehensive framework of organizationalcreativity support and a lack of examples on how to design OCSSs.

The main task of this paper is to provide a theoretically andempirically grounded discussion on organizational creativity and thedesign of information systems to provide organizational creativitysupport. The study proposes a comprehensive, conceptual frameworkfor the design of an OCSS. The idea of this study is to attempt to answerthe following questions: (1) What is the essence of organizationalcreativity and its IT-based support? and (2) How to use the resource-based view (RBV) and a multiagent approach for the design ofinformation systems for organizational creativity support? The searchfor answers to these questions is mainly conducted on the theoretical,methodological, and the empirical foundation. At the start, a criticalreview of the relevant literature is conducted to identify the organiza-tional creativity issue and its computer support. Then, the theory ofRBV and a multiagent approach are explored. The search for theappropriate literature employed different bibliographic databases,e.g., EBSCOhost, Emerald Management 75, ISI Web of Knowledge,ProQuest, and Scopus. Additionally, open access papers are explored.Finally, a comprehensive, conceptual framework for the design of

http://dx.doi.org/10.1016/j.im.2017.04.004Received 5 March 2015; Received in revised form 3 April 2017; Accepted 15 April 2017

⁎ Corresponding author.E-mail addresses: celina.olszak@ue.katowice.pl (C.M. Olszak), tomasz.bartus@ue.katowice.pl (T. Bartuś), pawel.lorek.@ue.katowice.pl (P. Lorek).

Information & Management 55 (2018) 94–108

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information systems to support organizational creativity is proposed.The framework is grounded in RBV and a multiagent approach. RBVenables us to better know how to manage information resources andwhat should be done with them to support organizational creativity. Inturn, a multiagent approach gives a sound basis to assist various humanactivities that are significant in the context of organizational creativitydevelopment. Finally, the idea of a prototype of a system based ondifferent intelligent agents, such as searching agents, monitoringagents, capturing agents, and discovering and suggesting agents todesign organizational creativity support, is described. The prototype, asan example of the proposed framework, was tested in a social mediaenvironment. Customer opinions from the consumer electronics sectorwere the center of the analysis. In the conclusion of this paper,theoretical contributions, practical implications, and future directionsof the study are presented and discussed.

2. Related works

2.1. Knowledge and organizational creativity

Creativity is closely related to knowledge. An organization's successdepends on its capability to create new knowledge that is thenmanifested in new, useful ideas and in innovations [1–3]. Knowledgeis an organization's most valuable resource because it embodiesintangible assets, routines, and creative processes that are difficult toimitate. It is claimed that successful organizations are those thatconsistently create new knowledge, disseminate it widely throughoutthe organization, and rapidly include it in new products. According toNonaka and Takeuchi [9], there are two forms of knowledge: explicitand tacit knowledge. Explicit knowledge is defined as knowledge thatcan be expressed formally. It is easily communicated and diffusedthrough an organization. Explicit knowledge may include trade jour-nals, executive reports, speeches, project reports, etc. [10]. On the otherhand, tacit knowledge consists of subject expertise, assumptions, andinsights. This knowledge is critical in decision-making, creating acompetitive advantage, and creativity and innovation. Organizationsthat want to survive on the market are required to have strategiccapability for continuous acquisition of knowledge resources and thecreation of new configurations [5–7]. This refers to discovering variouspatterns, generalizations, regularities, and rules in data resources[11,12]. Such data mining may be utilized to predict and to describereality (e.g., customers’ preferences concerning new products, changesin product features).

Baron [13] advocated that creativity means the creation of some-thing new, which is based on the exploration of different informationresources (databases and knowledge bases). Creativity is compared to aknowledge system [14] used for solving different problems andincreasing organizational effectiveness [15]. One of the most citeddefinitions of creativity says that the outcomes of creativity are ideasthat are distinguished by novelty and usability [16,17]. Many authorshighlight that these ideas are used to achieve some particular aims [18]and that they have a significant impact [19]. Mumford et al. [20] statedthat creativity is crucial in the solving of semi-structured or un-structured problems.

Although the term “creativity” is rooted in psychology, it is used indifferent organizational contexts—in the context of business strategy,business processes, strategic management, competitive advantage,organizational development, leadership, and innovation. According tomany scholars [21–24], “organizational creativity” means the capabilityto generate new and useful ideas that concern products, services,processes, managerial practices, and competitive strategies. Woodman[25] defined organizational creativity as the creation of a valuable,useful new product, service, idea, procedure, or process by individualsworking together in a complex social system. New ideas must constitutean appropriate response to fill a gap in production, marketing, or theadministrative processes of the organization [26]. Therefore, creativity

could be seen as an important organizational capability [1], a possiblesource of organizational effectiveness and a source of competitiveadvantage. It is a collaborative psychological process that takes placein an organization and is affected by contextual and organizationalfactors [27]. According to Brennan and Dooley [28], creativity withinan organizational context can be regarded as the sum of the followingfunctions: the creative person, creative task, and the organizationalcontext (culture). Sundgren and Styhre [29] noted that organizationalcreativity is something more than a collection of creative individuals.Thus, the mere presence of creative individuals in an organization doesnot guarantee organizational creativity because it is the result of thewhole spectrum of organizational factors. Amabile et al. [30] pointedout that the extent to which people produce creative ideas depends notonly on their individual characteristics but also on the work environ-ment that they perceive around them. Styhre and Sundgren [29] arguedthat managers and leaders play a crucial role in supporting andenhancing organizational creativity. They must adopt unique stylesbased on agility, perceptiveness, and rapid decision-making. Rosa et al.[31] identified four management principles that can engender creativ-ity and innovation in organizations: (1) to manage organizations so thattheir knowledge base is more diverse than what would occur naturally,(2) to encourage employees to embrace a collaborative and noncom-placent attitude toward work and the organization, (3) to make itpossible for organization members to engage in the quick testing ofideas and solutions as they emerge, and (4) to reward employees andsupervisors’ behaviors that support these principles and punish resis-tance to their implementation. On the contrary, some authors specifiedsome barriers to organizational creativity. They include intolerance ofdifferences, overly rational thinking, inappropriate incentives, andexcessive bureaucracy [28].

Many authors point to a link between creativity and innovation.Baer [32] stated that creativity can be viewed as the first stage of aninnovation process. Creativity is the starting point for any innovation.However, creativity is an individual and solitary process, and innova-tion is a more inclusive process involving many people. Brennan andDooley [28] indicated that ability to stimulate innovation is highlydependent upon the stock of potential ideas and problem solving, whichare products of an organization's creativity processes. To promoteinnovation as an output of creativity, the organization must itself becreative and imbibe a culture of innovativeness [33]. From theinnovation perspective, knowledge provides the organization with thepotential for novel action, and the process of constructing novel actionsoften entails finding new uses or new combinations of previouslydisparate ideas [26].

2.2. Creativity support systems

Shneiderman [34] stated that technologies that “enable people to bemore creative more often” are referred to as creativity support systems(CSSs). Technically, the term CSS concerns a class of informationsystems encompassing diverse types of Information systems (IS) thatshare the enhancement of creativity [35]. CSS may be used to (1)enhance a user's ability to perform creative tasks (the ability that theuser possesses already), (2) support users in domain knowledgeacquisition, to free up their creativity, and (3) give users newexperiences concerning creative tasks, thus giving them new task-solving capabilities [36]. Shneiderman [34] argued that CSS should

• offer indices for work progress measurement, together with thepossibility of generating alerts,

• contain libraries of images, thesauri, sketching interfaces, possibilityof ideas mapping,

• support communication and collaboration, enable group workcoordination,

• simplify knowledge coding in an electronic form.

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Muller and Ulrich [37] and Klijn and Tomic [38] argued that CSSmay be used for the following:

• information collecting—by simplification of searching, browsing,and visualization,

• defining linkages between information,• creative processes—by loose associations, examination of solutions,composing of artifacts, and idea reviewing,

• disseminating the effects of creative cooperation.

Greene [39] stated that organizational creativity support softwareshould be able to explore problem domains; teach and discover newproblems; support collaboration; visualize domain interdependences;and simplify storing, classifying, and mining of notions. Woodman et al.[25] pointed out that IT tools should enable first of all information flowand communication in an organization. Lubart [40] and Ulrich andMengiste [41] stressed the importance of “what-if” analyses, data andprocesses visualization, and creative processes affect dissemination,visualization of ideas, human–computer dialogue in the problemsolving process as well as advanced human–computer interaction,business plan support, and storing of users’ preferences. In turn, Dewett[42] claimed that three benefits appear to be particularly salient: theimproved ability to link and enable employees, the improved ability tocodify the organization's knowledge base, and improved boundaryspanning capabilities.

Davies et al. [43] claimed that CSSs refer to fuzzily defined domains,having unknown requirements, with fuzzily defined measures ofsuccess. CSSs are intended to support not precisely defined users;otherwise, their users behave in an unconventional way. Therefore, it isadvocated that CSSs stimulate users’ imagination, the creation of newideas, and model creative processes and enable brainstorming process,recombination of ideas, ranging of ideas according to different criteria,and identification of interdependences [44].

According to some scholars, two types of CSS are distinguished[34,35,45–47]: individual creativity support systems (ICSSs) and groupcreativity support systems (GCSSs). The main purpose of ICSSs is toincrease the cognitive process, individual inspiration, and the learningand reasoning of individual persons. The most popular tools used inICSSs include editors, visualization systems, brainstorming, e-mails,spreadsheets, databases and knowledge bases, scenarios, and modelingtools. In turn, GCSSs encompass several types of information systems,for example, group decision support systems, knowledge managementsystems, computer-mediated communication, which commonly supportthe process of idea generation and idea evolution as well as selection ingroups. GCSS combines the properties of individual creativity supportwith collaboration and coordination support.

The analysis of the relevant literature allows us to state that there isnot a comprehensive view on OCSS and its design. Of the papersreviewed in the literature search, none addressed this topic. Aconceptual framework for the design of information systems to provideorganizational creativity was not formulated, and no attempt was madeto validate it.

We propose to extend the previous classification of CSS andintroduce new type of CSS called OCSSs (Table 1).

OCSS opens a new emerging type of creativity support. In contrastto previous systems, OCSS is dedicated to the whole organization andits environment. Its purpose is to increase competitive advantage andan organization's performance by offering rapid access to different,heterogeneous, dispersed information resources, their analysis, knowl-edge discovery, its visualization, and suggesting some opinions thatmay be the foundation for the creation of new and useful ideas.

3. Theory of organizational creativity support systems design

To address the mentioned objective of this study, two theories areproposed and used: RBV and a multiagent approach. RBV is applied to

better know how to manage information resources and deal with themstrategically to support organizational creativity. A multiagent ap-proach is a critical and vital part of a theory for the design of OCSSs.Intelligent agents, as autonomous and proactive entities (software), areused to build assistance in performing various human activities,including searching, collecting, and analyzing data; discovering newknowledge; suggesting new ideas; communicating and disseminatingnew ideas; and conducting different interactions with human partici-pants.

3.1. Resource-based view

A new look at the issue of organizational creativity support openswithin the RBV. It gives the foundation for a sustainable and compre-hensive development of organizational creativity support and a soundbasis for stating what information resources and capabilities should befollowed in OCSS. RBV argues that the success of an organization'sstrategy depends on the configuration of its resources and capabilitiesthat are the basis to building key competences. Acquiring, configura-tion, reconfiguration, and the developing of available informationresources are critical factors in creating a competitive advantage andcreating value [48,49]. An extended approach of RBV resources impliesintangible categories including organizational, human, and networks[50]. According to RBV to provide competitive advantage, resourcesshould be Valuable (enable an organization to implement a value-creating strategy), Rare (are in short supply), Inimitable (cannot beperfectly duplicated by rivals), and Non-substitutable (cannot becountered by a competitor with a substitute) (VRIN). This knowledge-based resource approach of RBV encourages organizations to obtain,access, and maintain intangible endowments because these resourcesare the ways in which firms combine and transform tangible inputresources and assets.

The concept of traditional RBV assumes that the informationresources are static and do not consider changes in the turbulentenvironment of the organization. The answer to these challenges of theenvironment is dynamic capabilities (DC). The method of resourceusage is at least as important as the value of these resources [51]. Thebenefits derived from the resource pool are transient, so organizationsneed to focus on continuous acquisition of new resources (information,knowledge) and the creation of these new configurations [5,52]. DC canusefully be thought of as belonging to three clusters of activities andadjustments [52]: (1) identification and assessment of an opportunity(sensing); (2) mobilization of resources to address an opportunity and tocapture value from doing so (seizing); and (3) continued renewal(transforming). These activities are required if the organization is tosustain itself as markets and technologies change, although some firmswill be stronger than others in performing some or all of these tasks. Inthe context of organizational creativity support, sensing involvesexploring technological opportunities, probing markets, and listeningto customers. Seizing refers to [53] (1) solution development (acompany's capability to generate different potential solutions, e.g.,process design, concept development, idea refinement), (2) solutionevaluation and selection (established procedures to allow for informeddecision-making and thus for selecting the most adequate solution for aspecific problem), and (3) solution detailing (a plan needs to be forwardthinking and have control mechanisms). The last activity—transfor-ming—includes unfreezing, changing, and re-freezing subcapabilities.Breaking up existing work structures and procedures is an importantaspect when searching for new suggestions. The acceptance has to befostered by actively communicating the changes and benefits that resultfrom them. Changing subcapability refers to the actual implementationof the idea change. The last subcapability relates to all tasks necessaryto foster internalization of the newly implemented ideas.

In the context of RBV, we interpret organizational creativity supportas an information system that enables an organization to acquire,collect, and analyze different information resources and discover new

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knowledge to create new ideas that concern, among other things, newproducts, services, managerial practices, and competitive strategies.The RBV approach provides a valuable way for designers of OCSSs tothink about how information resources relate to organizational crea-tivity, performance, and competitive advantage. In particular, thisapproach provides a cogent framework to seek valuable and hard toimitate resources and to assess their strategic value. It helps designersand managers to understand the role of information within organiza-tions.

3.2. Multiagent approach

The concept of the agent in information systems dates back to the1970s, when studies on programs called “intelligent” were undertaken[54]. The idea of an autonomous object and an interactive actor waspresented in 1977 by Hewitt [55]. A new trend of research on agentsappeared around 1990, and it concerned the development of the theoryand design of architectures of different types of agents, and the abilityto communicate between them [56]. It is noted that although “agent” isfrequently defined in the literature, there is no universal explanation ofthe term “agent.” It is often associated with the following notions:intelligent agent, intelligent software, wizards, knowbots, taskbot,userbot, software agent, softbots-intelligent, or software robots. In aparticular case, the agent may be a human or an expert in the specificdomain. According to Hewitt [55], an agent is an interactive objectbased on parallel processing, having some internal state and the abilityto respond to messages of other objects (actors). The agent is an entitythat performs some actions in a particular environment and is aware ofthe emerging changes. Moreover, it can react to such changes [57,58].The agent has a set of goals, certain capabilities to perform actions, andsome knowledge (or beliefs) about its environment [59]. In addition,Thomsen [57] describes the agent as “a solution-oriented ensemble ofcapabilities including natural language processing, autonomous reason-ing, proactive computing, discourse modeling, knowledge representa-tion, action-oriented semantics, multimodal interaction, environmentalawareness, self-awareness, and distributed architectures.” IBM [60], inturn, defines intelligent agents as “software entities that carry out someset of operations on behalf of a user or another program, with somedegree of independence or autonomy, and in so doing, employ someknowledge or representation of user's goals and desires.”

Many authors emphasize the specific properties of the agents[59,61–63]. They include

• autonomy—the agent operates as an independent process, with nodirect human intervention and has control over its actions andinternal state,

• reactivity—the agent perceives its environment and promptlyanswers the perceived changes to its environment,

• pro-activity—the agent not only reacts to the changes to itsenvironment but also manifests a goal-oriented behavior and takesinitiative,

• social ability—the agent can interact with other agents or humans,using an agent communication language,

• self-analysis—the agent is capable of analyzing and explaining itsbehavior and capable of detecting its errors or success,

• learning—the agent can adapt and improve by interaction with itsenvironment,

• temporally continuous—the agent runs continuously in its environ-ment, as long as necessary, to implement the task,

• mobile—the agent has an ability to move between differentenvironments.

The belief, desire, intention (BDI) model is one of the most well-known models that is used for programming agents. The model consistsof three components [64,65]: (1) Belief—represents the informationalstate of the agent (including itself and other agents). It may includerules of requesting, which lead to new belief formation; (2) Desire/Goal—reflects the agent's motivation state. Examples of desires mightbe “find the best price, best customer”; (3) Intention/Plan—means thatthe agent starts to perform an action plan. Plans are sequences of theactions that the agent can perform to achieve one or more of its goals.

It has already been pointed out that its knowledge, its computingresources, and its perspective limit the capacity of an intelligent agent.Therefore, it requires forming communities of agents. These commu-nities, based on a modular design, can be implemented, where eachmember of the community specializes in solving a particular aspect ofthe problem. The agents must be able to interoperate and coordinatewith each other in peer-to-peer interactions [58]. This idea of theagents’ operation is nowadays described as a multiagent system. Amultiagent system can be defined as a loosely coupled network ofentities that work together to solve a problem that cannot be solved byan individual agent. These entities can show self-organization andcomplex behavior, even if the individual agent's strategies are simple[66]. A multiagent system is a network of agents that are reasoning(problem solvers), cooperating, communicating, and negotiating to

Table 1Creativity support systems.Source: Elaborated on [34,35,45–47].

Individual creativity support system (ICSS) Group creativity support system (GCSS) Organizational creativity support system (OCSS)—newquantity of CSS

Essence Creative problem solving, idea generation,creativity limited to narrow domain andindividuals

Work groups, sharing knowledge, communication,creativity limited to selected organization's units,departments

Continuous acquisition of new information resources andthe creation of these new configurations (newknowledge, suggestions) to develop new and useful ideasconcerning products, services, managerial practices andcompetitive advantages

Scope Focused on single individual Selected groups and teams Dedicated to the whole organization and its changeableenvironment

Purpose Increasing the cognitive process, creativeproblem solving, individual inspiration,learning and reasoning

Creating shared idea space, increasing groupcommunication, group consensus

Increasing competitive advantage and an organization'sperformance by offering rapid access to different,heterogeneous, dispersed information resources, theiranalysis, knowledge discovery, its visualization, andsuggesting some opinions that may be the foundation forthe creation of new and useful ideas

Theories Cognitive theory, behavioral theory,motivation theory

Group decision-making theory, communication theory,design of group decision support systems

Strategic management, resource-based view, dynamiccapabilities, design of management information systems

Tools Editors, visualization systems, brainstorming,e-mails, spreadsheets, limited data bases andknowledge bases, scenarios and modelingtools

Group Decision Support Systems, KnowledgeManagement Systems, chat rooms, synchronic andasynchronic trainings, videoconferences, discussionforums, blogs, data marts

Knowledge bases, data warehouses, Business Intelligence,analytics, multiagent technology, neural networks,search engines, visualization tools, data mining, webmining, opinion mining

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achieve a specific task. Individual agents can adapt their behavior to thechanging environment in which they work [67,68]. A good example ofagents’ co-operation is a teamwork in which a group of autonomousagents cooperate, both to develop their own individual goals and for thegood of the whole system [69].

The above characteristics of the multiagent approach lead to theconclusion that such an approach can be widely used in the design ofcomplex information systems for organizations. Particularly, it providesa basis for extending OCSSs with flexible, distributed, and intelligentfeatures. Individual and intelligent agents may assist to search, acquire,monitor, discover, and deliver personalized information to varioususers according their preferences and profiles. They may be focused onproviding filtered information of given domains (news, social events,and trends on the market). The advantage of the multiagent approachfor the design of organizational creativity support is also the fact thatthe intelligent agents enable contacts with other agents, databases, andhumans through various communication channels.

3.3. Proposition of a comprehensive framework of information systemdesign to provide organizational creativity support

The suggested framework provides a comprehensive view on thedesign of information systems to provide organizational creativitysupport. This framework consists of six stages that are stronglyinterconnected and are of iterative nature. They include the following:

• Problem finding and identification of major creative needs oforganizations.

• Acquiring information resources.• Information analysis, knowledge discovery, providing some sugges-tions concerning new ideas.

• Evaluating and selecting discovered knowledge.• Communicating newly discovered knowledge in an organization andconsidering whether the new knowledge should be transformed intoinnovation; and

• Evolving creative knowledge in an organization and organizationallearning.

The proposed framework is discussed in the following respects(Table 2):

• What are the human activities that are conducted in each stage ofthe proposed framework and how do they move the creative processforward?

• What are the information resources at play in each stage, what needsto be done with them, and how RBV helps manage informationresources and deal with them strategically?

• What are the ways in which an OCSS might assist, given theseactivities and resources needs? More specifically, what sorts ofagents should be incorporated into OCSS to accomplish this.

3.3.1. Problem finding3.3.1.1. Human activities. Problem finding should be considered one ofthe most important steps in the whole model. It is strongly associatedwith creative needs of any organization. Problem finding requires anorganization/user to specify its creative needs. The creative needs canbe expressed in the form of some phrases, whole sentences, andkeywords (concerning its business areas, business problems to solve,etc.), which may be the foundation to find some problems. This involvesfinding out which areas of an enterprise's functions need changes (orwhich changes should be necessary in the near term) that wouldinfluence, for example, new products, new services, or newmanagerial practices. These changes may stem from a necessity toimprove an organization's competitive advantage, relationships withcustomers and suppliers, from willingness to become a leader in aparticular sector, and from a desire to enter specific alliances. Various

methods and techniques may be applied to know the creative needsbetter. They include the following: interviews, questionnaires,observations, and documentation analyses.

Creative needs should be examined on the level of an individual,particular employee groups, and the whole organization along with itsenvironment. This also involves analyzing closer and more remoteenvironments. Both internal and external information resources may bethe inspiration for problem finding. This implies some necessity to tracknot only business reports, documents, and databases but also somepublic reports, scientific databases, patent collections, and the Internet,as well as to consider general trends on the market and activitiesundertaken by the competition and to pay attention to comments fromreaders on online news [70]. It is also worth mentioning that byproblem finding, it is necessary to analyze the organizational culture,organizational climate, an organization's styles of management, and itsapproach to sharing knowledge, group work, and communication.

3.3.1.2. Resource-based view. A significant role is played by RBV at thisstage. RBV facilitates the identification and the assessment ofinformation resources in the context of problem finding. Carefulexploration should be given to what kind of information resourcesdoes RBV clarify and why, as well as what information is extremelyimportant for an organization and how this information may help inproblem finding. Furthermore, RBV should be helpful in thespecification what kind of information is missing, what is incomplete,and from whom/where this information may be acquired. RBV providesknowledge on what kinds of resources are available and useful inproblem finding.

3.3.1.3. Intelligent agents. It is really important to support problemfinding, although this phenomenon is relatively underestimated in therelevant literature [43]. Some attention is paid to a possibility ofdecomposition, problem hierarchization, and creation ofgeneralizations with the help of IT [71]. Intelligent agents mayreplace humans in performing difficult, tedious, and time-consumingactions concerning searching and tracking various informationresources that may provide an inspiration to find problems. On thebasis of a user query (e.g., in the form of keywords, whole phrases,sentences, or business topics), intelligent agents can visit variousinformation resources. The obtained results (links, documents, files,or databases) are grouped, stored, and then the most valuable sources ofinformation are presented to the user. Intelligent agents are helpful insorting and prioritizing various documents significant for problemfinding. They themselves may specialize in searching specific sorts ofinformation resources (e.g., social media, price comparison websites,patent catalogues, and knowledge portals), communicate with eachother, and activate other agents to perform particular activities. Suchagents may suggest and help detail (concretize and substantiate) thepaths of problem finding, create knowledge maps, and semantic maps.Intelligent agents are also responsible for the permanent collection ofuser requests, choosing the most adequate methods and tools to servesuch requests, and finally collecting the response and sending it back tothe user.

The result of this stage should be hierarchization of informationresources, decomposition of information (e.g., into smaller, relatedgroups, and subproblems), knowledge maps, semantic maps, and a listof the main sources of information that may be helpful in problemfinding. Such activities should facilitate the process of problem findingand further acquiring appropriate and detailed information.

3.3.2. Acquiring information resources3.3.2.1. Human activities. When there is no knowledge of a given topic,particular individuals must quickly assimilate relevant information andundertake an attempt to acquire the information in question [10,26].The acquisition of information resources is one of the most difficulttasks to be undertaken in the whole model. This results from several

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factors, predominantly including the following:

• considerable dispersion and diversity of information resources,• no access to numerous information resources,• poor quality of data in different repositories (e.g., incoherence,inconsistent).

These steps call for the acquiring of both internal and externalresources (indicated at the problem finding stage). The former mayinclude paper files, documents that describe the enterprise's missionand strategy of development, selected sales documents, financialdocuments, enterprise resource planning systems, databases, datawarehouses, business intelligence systems, customer relationship man-agement systems, supply chain management systems, decision supportsystems, and case history. Organizational creativity increasingly fre-quently requires acquisition of information that stems from externalresources [8]. Such resources may include databases of patents,company reports, government records, library archives, scientificdatabases, and Internet resources including social media, blogs, andcomparison websites. However, it is necessary to remember theimperfection of the human memory that is characterized by limited

capability of storing different information. Therefore, different IT toolsare recommended to acquire and collect huge amounts of diversifiedinformation.

3.3.2.2. Resource-based view. RBV reminds us that althoughorganizations possess many information resources, only a few of themhave the potential to lead the organization to a position of competitiveadvantage. RBV helps an organization to determine what resources arevaluable, rare, inimitable, and nonsubstitutable and what informationshould be acquired, configured, and reconfigured to capture value fromthem.

3.3.2.3. Intelligent agents. IT enables acquiring and coding hugeamounts of diversified information that gets compared withorganizational memory. In this context, impressive capacities areoffered by intelligent agents together with databases, knowledgebases, data warehouses, in-memory applications, and knowledgeportals [72]. Intelligent agents may be incorporated into theaccomplishment of tedious and time-consuming tasks that concernacquiring, monitoring, and browsing huge amounts of information(indicated as important for finding problems at the first step of this

Table 2The research framework—a comprehensive framework—for the design of an information system to provide organizational creativity support.

Human activities RBV Intelligent agents

Finding problems - Identification of creative needs- Specification of terms, phrases, keywords,ontology, business domains that express newbusiness needs

- Identification and assessment ofinformation resources that would be aninspiration for problem finding

- Searching and tracking huge amountsof information- Sorting and prioritizing of variousdocuments significant for problemfinding- Storing the results of searching in theform of knowledge maps and semanticmaps

Milestone Hierarchization of information and knowledge, decomposition of knowledge, ontology, knowledge maps, semantic maps, list of terms,classes of terms, and relations between classes

Acquiring information resources - Development of organizational culture based onfacts and sharing knowledge (motivation to collectand to share information, communication)

- Elaboration of procedures andmethods and the whole strategy toacquire information- Identification of gaps in informationand knowledge

- Acquiring, monitoring, browsingselected information resources- Reacting quickly to some changes ininformation contentsPromptly answers the perceivedchanges

Milestone A repository to store up-to-date, reliable, complete, and relevant information useful while searching new ideas

Information analysis, knowledgediscovery and providing somesuggestions concerning new ideas

- Development of knowledge center- Motivation to create new knowledge and to shareknowledge

- Elaboration of procedures andmethods to discover new knowledgeand its utilization- Describing what information sourcesshould be explored in detail

- Searching some associations andpatterns between data, reasoning, andlearning- Visualizing discovered associationsand patterns- Suggesting some recommendationsconcerning new ideas

Milestone Learning valuable facts from data and from newly discovered knowledgePool of recommendations for new ideas

Evaluating and selecting discoveredknowledge

- Identification of implications of new knowledgeon enterprise performance, competitiveadvantage, etc.

- Determining what criteria should beused to evaluate discovered knowledge

- Providing different documents,reports needed to evaluate newknowledge/ideas- Initiating interactions with humanparticipants- Exchange knowledge among variousagents

Milestone Pool of best suggestions from the whole pool of the generated ones

Communicating newly discoveredknowledge in an organization

- Reach all potential departments and individualswho might be interested in utilization of newknowledge/idea

- Determining what kind of newknowledge and to whom it should besend- Orchestration of knowledge

- Helping in sharing and protectingknowledge- Presenting particular reports, andresults- Sending e-mails to participants- Initiation of talks and forums

Milestone Knowledge dissemination

Evolving creative knowledge in anorganization and organizationallearning

- Integration of organization's knowledge withknowledge of customers, suppliers, alliances, etc.

- Knowledge nets- Learning organization

- Learning and interactions with othersagents and programs- Exchange knowledge

Milestone Growth of knowledge, integration of knowledge, new creative needs, and overcoming actual knowledge borders

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framework). Users can also schedule intelligent agents to execute on aone-time basis, periodically, or based upon events. Many intelligentagents provide a set of options through which the users can scan thehuge data warehouses and websites. Agents may quickly react to somechanges in information content, promptly answer the perceivedchanges, and store the acquired information in databases. Thesedatabases may be analyzed later in detail and filtered by users. Aresult of this stage of the proposed framework should be theestablishment of a repository that enables storage of up-to-date,reliable, complete, and relevant information useful while searchingnew ideas.

3.3.3. Information analysis, knowledge discovery, and providing somesuggestions concerning new ideas3.3.3.1. Human activities. The third step of the proposed frameworkinvolves the analysis and discovery of new knowledge. The discovery ofnew knowledge may refer to discovery of (1) new functionalities/features of products, (2) new organizational practices (e.g., newcustomer service, new forms of cross-selling), (3) new logistics chainsand alliances, (4) new technologies, and (5) changes in products design.To understand the importance of discovering new knowledge for anorganization, it is necessary to be aware of its relationships withenterprises, industries, or the whole environment [73]. Knowledgemay contribute to going beyond traditional business functions andprocesses, expanding the scope of activities undertaken byorganizations, enriching its present functions, and transforming itssupply chains into dynamic ecosystems.

The process of knowledge discovery should engage a broadlyunderstood community of an organization that includes decision-makers, employees, customers, suppliers or external experts, andrepresentatives of public institutions [10]. Their diversified knowledge,competencies, and experience may prove useful while proposingoriginal expertise that cannot be created by employees acting on theirown. Knowledge discovery requires accessibility to diversified informa-tion resources. Therefore, different stakeholders of an organizationshould be informed which resources they can use. While generatingnew knowledge, different individuals, departments, and units (manage-rial personnel, entrepreneurship incubators or departments responsiblefor improvement recommendations) may turn out to be helpful. Thisstage of the model proposed, as probably none of the other stages,requires employees to be particularly motivated to create new knowl-edge and share their knowledge. A significant role is to be played hereby management that they are supposed to support the development oforganizational knowledge. Organizational culture, organizational cli-mate, and freedom to act are factors that determine organizationalcreativity.

3.3.3.2. Resource-based view. At this stage, RBV focuses on theimportance of growth and reconfiguration of knowledge for anorganization and its creativity is. RBV highlights that discovery ofnew knowledge refers to two activities that may be supported both forhuman or IT tools [74]. The first concerns the widely understood dataexploration, and the second concerns data exploitation. Dataexploration enables an organization to overcome the border of actualknowledge and its capabilities. This may refer to new technicalcapabilities, market experiences, and new relationships with theenvironment. In addition, the exploration is a conscious search fornew knowledge sources, enriching of existing resources, adoption ofnew behavioral orientations, and acquisition of new competencies. Dataexploitation concerns the use of existing knowledge bases. It is limitedto actual resources and refers to their detailed analysis. RBV stressesthat transformation of information resources enabling the developmentof a unique information resource base is the essence of this stage. Suchan information base may be the foundation to provide new ideas.

3.3.3.3. Intelligent agents. Intelligent agents can contribute to the

growth and expansion of knowledge. Intelligent agents combinedwith data mining, artificial intelligence, and data visualizationtechniques [75] allow not only for the exploring of different sourcesof data but also for the identifying of specific relationships,interdependencies, and patterns in data [11]. Organizations maylearn valuable facts from such data. They may point to, for example,different trends that are observed on the market, customer behaviors, orcustomer purchase preferences. On the other hand, intelligent agentscombined with visualization techniques enable better perception andunderstanding of all interdependencies between different data.

It is also worth mentioning group work tools that may be used todiscover new knowledge. These tools show that creativity is notobtained in social isolation. Individuals and groups continuouslyparticipate in creative and interactive processes. Employees createknowledge, present it to other members of their teams, and learn fromothers to eventually modify and enhance their primary ideas. Groupwork tools combined with intelligent agents allow members of projectteams to communicate easily, thus overcoming barriers of time andgeographical location. People may work in networks and use jointresources. By integrating information from various databases and fromdifferent users, intelligent agents may suggest some recommendationsconcerning new ideas.

3.3.4. Evaluating and selecting discovered knowledge3.3.4.1. Human activities. Organizational creativity is an iterativeprocess full of attempts and mistakes [43]. Hence, the process inquestion requires control, evaluation and selection of the analyzedinformation/discovered knowledge, and the best suggestions from thewhole pool of the ones generated. It is difficult to suggest generalcriteria be taken into consideration by organizations. This is largelydetermined by organizational specifics. Novelty and usefulness ofsuggested ideas may be manifested in a different manner, e.g., newproduct functions, content, product esthetics, user-friendliness, orimproved product safety. It is worth identifying what implicationsthey may have in the context of enterprise performance.

3.3.4.2. Resource-based view. At this stage, RBV gives guidance as tohow information resources and discovered knowledge should becompared to one another and perhaps, more importantly, how theycan be compared with non-information resources. RBV sets out a clearlink between information resources and competitive advantage,providing a useful way to measure the strategic value of informationresources. It is helpful in indicating how newly discovered knowledgemay improve among other things, organization performance, sales,relationships with customers, and the position of an organization on themarket.

3.3.4.3. Intelligent agents. Intelligent agents incorporated withknowledge portals, visualization tools, and group work tools(including virtual conferences, discussion forums, and communities ofpractice) may turn out to be useful for evaluation, analysis of collectedinformation, and newly discovered knowledge [45]. They allowmembers of project teams to communicate easily, work in networks,discuss, compare obtained results (new ideas), and evaluate them.Intelligent agents with visualization tools enable not only (asmentioned before) better perception and understanding of discovereddata but also evaluation of newly discovered knowledge. They mayprovide different documents and reports needed to assess (evaluate)new ideas. Additionally, they may be used to initiate interactions withparticipants.

3.3.5. Communicating newly discovered knowledge in an organization andconsidering whether the new knowledge should be transformed intoinnovation3.3.5.1. Human activities. Communicating new knowledge anddeciding whether it should be transformed into innovation make up,

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in principle, the core stage of the whole model. Such communicationshould reach all potential departments involved (production,marketing, customer service, etc.) and individuals who might beinterested in its utilization.

3.3.5.2. Resource-based view. RBV argues that having knowledge isinsufficient if it does not lead to any innovation. What counts ishaving the ability to use this knowledge creatively [8]. Such ability isa key to creation of a competitive advantage. Simultaneously, theimportance of velocity of organizations reallocating their resources tolocations and areas where they can get the highest value should berecognized here.

3.3.5.3. Intelligent agents. Intelligent agents may serve to distribute(send) new knowledge/ideas to individuals and whole communities.Intelligent agents allow for easier rooting of creativity in organizationalculture. They may facilitate values, beliefs, and standards because theyallow for quick transmission of different information to a group ofpeople. Intelligent agents can be incorporated in knowledge portals,intranets, information bulletins, newsletters or employee forums,initiating various discussions and forums between them.

3.3.6. Evolving creative knowledge in an organization and organizationallearning3.3.6.1. Human activities. The last stage of the proposed frameworkfocuses somewhat on the fact that organizational creativity is not aclosed cycle but a continuous and dynamic process that should lead tothe development of creative knowledge in any organization. OCSSsshould be modified in an interactive manner. It means thatorganizations should continuously diagnose and predict their creativeneeds and develop the skills and the capabilities to build theirinnovative potential. This stage calls for identification of variouslimitations that may result in generating and evolving newknowledge, such as lack of competent persons or experts, insufficientaccess to some resources of knowledge, inappropriate orientation ofknowledge resources, or lack of sufficient financial means.

3.3.6.2. Resource-based view. RBV stresses that knowledge builds overtime in the head of employees in the form of past decisions, processes inthe organization, characteristics of products, interests of customers, andother experiences. This completes the generation of one piece ofknowledge but simultaneously attempts to integrate knowledge thatcomes from different users and various research domains. RVB remindsus that organizational creativity requires continued renewal andexpansion of knowledge. It provides the capability for organizationallearning, knowledge integration, and the creation of new creativeneeds.

3.3.6.3. Intelligent agents. At the last stage of proposed framework,intelligent agents may be incorporated with several applications andknowledge portals that provide access to different, heterogeneous,distributed databases, warehouses, and documents. They may behelpful in combining various resources from various researchdomains. Simultaneously, intelligent agents may propose usersparticipate in building and reconfiguring knowledge sources that canbe used by various users and for different purposes. This step also leadsto thinking whether new agents should be designed aimed at searchingand analyzing new and poorly known domains and topics but isimportant for the further development of an organization.

4. Proof of concept of organizational creativity support systemdesign

In this section, the prototype called organizational creativity insocial media (OCSM) was presented as an example of the proposedframework being applied. The area of consumer electronics was chosen

to illustrate how the proposed framework is used to build such aprototype and how organizations from this sector may draw inspirationto improve its products and services through in-depth analysis ofconsumer opinion.

Consumer electronics (smartphones, tablets, laptops, cameras, etc.)is one of the most dynamic sectors on the global market. Organizationsthat want to survive on this market and become a leader should respondquickly to feedback. According to the Boston Consulting Group [76],Polish Internet users are one of the most active groups of consumers inEurope. It is demonstrated not only by the quantity of products boughtby Polish consumers (mobile phones, laptops, digital cameras, othergadgets, etc.) but also by their activity in discussion forums, where theyexpress their opinions on the products purchased, compare them tocompetitors, or even propose some ideas related to their improvement[77]. Ceneo.pl is one of the largest forums in Poland where consumersreport their opinions on the products they buy. This forum is used totest OCSM.

4.1. Example of organizational creativity in social media design

The example of OCSM design is discussed on the basis of the mainsteps of the proposed framework. In other words, this exampleillustrates how the prototype aligns with the proposed framework andhow RBV and intelligent agents are used to build such a prototype andhow specific users in an organization might interact with the prototypeat each step. Four main sorts of intelligent agents, which are incorpo-rated into the design of OCSM, are described (Fig. 1). They include:searching agents, monitoring agents, capturing agents, and discoveringand suggesting agents. These agents, attributed with functions, rules,and methods to work in the electronic consumer domain, are autono-mous entities. They may work independently or collaborate with otheragents and users. A manager agent is responsible for the reliability ofthe whole prototype and manages the operation of the individualagents. Different techniques and tools are applied to develop such aprototype. They concern mainly web mining, text mining, web scrap-ing, web opinion mining, neural networks, search engines, conceptualgraphs, and visualization techniques.

When designing an OCSM, it was assumed that it should be flexibleenough to allow (1) automatic searching and monitoring of variousinformation resources (internal resources and external resources like:the Internet, social media) according to specific phrases and keywordsto find particular problems in the domain of consumer electronics; (2)automatic acquisition of various data originating from different,dispersed, heterogeneous resources that may be helpful in searchingnew ideas concerning new products from consumer electronics; (3)automatic analyzing and discovering new knowledge (e.g., discovery ofnew product features); (4) automatic visualization of discovered knowl-edge (e.g., correlations among product features and consumer opi-nions); (5) automatic suggesting/recommending (e.g., what productfeatures should be primarily improved, changed or what featuresshould be removed); (6) automatic delivery of new knowledge to users,who based on their own knowledge and experience, make decisions onhow it finally may be used; and (7) storing of collected, and analyzedknowledge to use by different users.

4.1.1. Organizational creativity in social media in problem findingOrganizations from the consumer electronics sector in search of new

problems and ideas should explore not only their own internalinformation resources, but their interest should increasingly focus onexternal resources (e.g., public data bases, patent bases, the Internet,and social media). The Internet, social media, and Ceneo.pl are placeswhere customers express their opinions and preferences about variousproducts (smartphones, tablets, etc.). Such opinions and preferencesmay be helpful in finding new designs, product functions, and solving aspecific organization's problems. Problem finding in OCSM is assistedmainly by searching agents. Searching agents, in an automatic way,

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perform tedious, time-consuming operations that concern searchinghuge amount of information, identifying their origin, access, andcredibility. Users give particular key words and phrases that refer tothe issue of consumer electronics (e.g., smartphone); with the help ofsearching agents, the list of relevant and adequate databases, papers,

patent collections, links, and web sites (Fig. 2) that may be importantfor problem finding are obtained. They may concern, for example, newtechnologies, trends in development of smartphones, and other mobiledevices. Searching agents also help in the grouping problem domains,the mapping of fragments of knowledge, and visualizing the path of

Fig. 1. The architecture of OCSM.

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access to needed information. They facilitate the users to find linkagesbetween knowledge portions according to the user profile.

At this stage, RBV helps establish what kind of informationresources (patents, databases, links, papers, and other documents)should be searched in the context of given user requests and whatnew requests (including new phrases and keywords) should be con-sidered during problem finding.

4.1.2. Organizational creativity in social media in acquisition ofinformation resources

OCSM uses two groups of agents to acquire information resources.They include capturing and monitoring agents. The task of capturingagents is to search and collect important information about features ofthe products and their characteristics; product characteristics of thecompetitors; characteristics of the Ceneo.pl user's activity; character-istics of the competitor's activity; characteristics of others users(customer and suppliers) related to the date and time of the activitydetection, area of activity, status, and the date and time of the capturingagents’ activation. They mostly acquire certain keywords, differentphrases (“positive” and “negative”), or files. A user is provided with anapplication that allows for clicking a “Run Agents” button to initiate thecapturing agent manually any time it is required. A user may quicklyand intuitively download from the website information that represent

the customers’ opinions concerning given products.Monitoring agents are used to track the changes in opinions of the

users on some smartphones in selected social media or price compar-ison websites. When the agent detects new content (e.g., new featuresof such product, new opinions on such product) posted in the pricecomparison website or in social media, it sends a signal that activatesthe capturing agent. This group of agents includes agents that monitorchanges on (1) the opinions on some organization's products andservices (features, quality), (2) the opinions on some products of thecompetition, and (3) the opinions of the users (including customers andsuppliers) of the other products.

Information collected by individual capturing agents are stored inCSV, SQL, XML, and XLS files and then are loaded to a database calledopinion storage. Then, such data are processed and analyzed bydiscovering and suggesting agents.

4.1.3. Organizational creativity in social media in the discovery of newknowledge and its evaluation

This stage of the proposed model is supported by discovering andsuggesting agents. These agents are responsible for performing variousanalyses and discovering new knowledge that refer to the opinions ofusers, customers, and competitors on selected products. More preciselyspeaking, they: (1) identify features of the selected smartphones andlink (associate) these features with the opinions of various customers,(2) carry out analysis on products and perform appropriate analysisfocused, for example, on improving some features, (3) suggest somerecommendations concerning changes/modifications to product fea-tures, and (4) draw up the characteristics of the customers (usersactivity, changes in preferences). Table 3 presents an example of a set ofsuggestions generated by the agent for a selected smartphone in thecontext of positive (“I like this”) and negative consumer opinions (“I hatethis”).

The user receives a suggestion that in the first place, productfeatures like appearance, display, functionality, should be improved.Then a product may be made more attractive for users by theorganization adding brand unique features such as a fingerprint readeron the screen. The agent demonstrates that these features such asfunctionality, design, battery life, android, price, camera, and no USB3.0 cable were evaluated as negative and suggests priority to remove/improve them. Interestingly, these features have been also ratedpositively by some users. This indicates that these features are veryimportant for users and their evaluation vary individually. Thus, furtherexploration/data selection should consider criteria such as age, educa-tion, place of residence, and consumer gender. Discovered knowledgefrom such exploration may be useful to develop new and morepersonalized products. It should be highlighted that a positive ratingshould not always mean the need for further product improvement. Thesame concerns the issue of a negative rating. The final decision on the

Fig. 2. OCSM in problem finding.

Table 3Suggestions generated by discovering and suggesting agent.

“Positive”features—suggestions—should be stillperfected

“Negative” features—suggestions—shouldbe removed or significantly improved

Product feature Priority Product feature Priority

Appearance Very high Functionality Very highDisplay Very high Appearance Very highFunctionality Very high Display HighAndroid High Life battery HighBattery life High Android MediumApparatus High Price MediumExecution High Apparatus MediumScreen brightness Medium Lack of USB 3.0 wire MediumWaterproof MediumSmooth operations MediumSpeed MediumSD slot card MediumReplaceable battery MediumCamera photo MediumFirm's applications MediumFingerprint leader MediumScreen MediumPulsometer – heart

rate monitorMedium

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improvement or alteration of the product (e.g., given feature of thesmartphone) should be supported by various additional analyses thatcontemplate business profits, fashion, or media image. The proposedanalysis should be treated as important; however, only one of the manyanalyses should be considered by product development.

To make the conducted analysis more clear and intuitive for users,different visualization techniques were applied. Graph structures areuseful methods to visualize data. To construct a graph structure,NetworkX 1.8.1 library was used [78]. Graph clustering was performedwith the usage of the ForceAtlas2 algorithm that is available in theGephi package [79–81]. All operations involving data processing wereperformed in the Python 2.7 environment.

Graph structures made the conducted analysis more intuitive andenabled the visualization of object–feature relationships (Fig. 3). Agraph always has three major vertices (nodes): a node that defines theproduct, a node that refers to advantages, and a node that refers todisadvantages. Other nodes refer to terms that describe a product.Graph edges have their own weights. Weights of connections in thegraph mean the frequency at which a particular term appears. The morefrequently appearing the feature, the higher is the weight of theconnection with a node of advantages or disadvantages. The visualiza-tion presented uses colors to distinguish features attributed to objects.Each color allows for classifying a particular node to a given class ofobjects, e.g., in this case, advantages or disadvantages. Valuableinformation for a user is also provided by the size of ‘advantages’ or‘disadvantages’ nodes. The size allows for quick assessment to find out ifa particular object is connected with more positive or negative feed-backs.

Many inspirations for the product development/improvement maybe provided by the analysis of comparing various products together(e.g., competitive products and even products from different segments)(Fig. 4).

Fig. 4 shows a graph where the nodes are described by the names ofvarious products (e.g., competitive products) and associated with theseproducts, the customer opinions. Smaller nodes labels are not visibledue to zooming out the entire graph. The largest nodes represent thefeatures of the products with the highest frequency of occurrence. Inturn, the smallest nodes represent the features of the products with thelowest frequency of occurrence, usually associated only with oneproduct. The edges illustrate the relationship of the products withcustomer feedback. A characteristic feature of this analysis is thepresence of a large number of products with universal features (thecentral part of the graph—price, portable, stylish, fast) and productswith unique characteristics prevailing (fingerprint scanner, HD video,extra security—the outskirts of the graph). In the present case, it isdifficult to indicate what specific links exist between the variousproducts. The identification of such relationships is, however, essentialwhen comparing the product with other products. These characteristicsmay be unique (not found in other product). There may also be areverse situation—they can be commonly found in other products. Theexample of visualization with a higher degree of data selection is shownin Fig. 5. The data selection process consists of two stages. In the firststage, the chosen products are selected for detailed analysis. Thepurpose of this stage is to reduce the size of the graph. In the secondstage, it is possible to zoom in the simplified graph and explore therevealed relations.

Three types of nodes are presented in Fig. 5. The first type of nodepresents the analyzed products. The second type of node describesunique product features (strongly associated with the specific product).The nodes of this type are found in the direct neighborhood of the noderepresenting a given product (e.g., the ring of nodes surroundingProduct 1 in Fig. 5). The third type of node includes those assignedto more than one product. Where a particular feature is present in morethan one product, the node that represents it is placed at a distance

Fig. 3. Visualization of the relationship between the features of the selected product and the users’ opinions (“positive,” “negative”).

Fig. 4. Example of data visualization.

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proportional to the frequency of co-occurrence with each of theproducts. For example, the node representing the attribute “Speed” isplaced in the central part of Fig. 5, as it occurs in Products 1, 4, and 5.Moreover, this node has the same color as the node representingProduct 5. The reason is that attribute “Speed” is most often associatedwith Product 5. These nodes form specific links between the productsand can be used to determine the relationship of similarity between oneproduct and another.

Finally, newly discovered knowledge, its visualization, and somesuggestions (e.g., concerning improving some products, introducingnew features, and new functionalities), performed by OCSM, are storedin a database—called storage. Such collected knowledge, as well as theknowledge and experience of the user, may be the foundation togenerate new ideas and then innovations.

4.1.4. Organizational creativity in social media in communicating newlydiscovered knowledge and evolving creative knowledge in an organizationand organizational learning

Discovered knowledge is mapped and presented in a knowledgeportal. A knowledge portal is a space that some news (e.g., aboutinnovations, business plans) and business activities are discussed,shared, and evaluated by managers, employees, and others users.Such a work space can facilitate communication and collaborationamong organizational workers. Discussion forums, wiki, documentmanagement systems, intranets, and extranets are the main componentsof such a portal (Fig. 6).

Intelligent agents incorporated in the knowledge portal help infinding people with a specific expertise and invite them to discussions,

meetings, and knowledge sharing. Intelligent agents are helpful insupporting education and training. They provide a knowledge base forthe less experienced users or for a user who just wants to learn moreabout a particular subject. Intelligent agents in a knowledge portalcontribute not only to exchanging knowledge but also to combiningnew knowledge with already existing knowledge (e.g., customerrelationships management systems or other enterprise systems) orreplacing already existing knowledge with new knowledge.

4.2. Validation of organizational creativity in social media

The presented prototype is a kind of initial validation in the form ofa proof of concept. It needs further validation and further application inpractice. Regardless of this fact, we can now make an initial assessmentof the OCSM. We notice both its advantages and disadvantages.Undoubtedly, among the OCSM strengths, there are

• system flexibility allowing to scan and extract data from variousinformation resources such as the Internet and any social media,

• fast acquisition of data from selected social media,• quick monitoring and reacting to changes in the contents ofparticular websites,

• cooperation and communication between agents,• rapid combination of various resources,• efficient knowledge discovering and codifying,• visualization of domain interdependences.

When testing the OCSM, we also noticed its weaknesses. These are

Fig. 5. Example of another (further) data visualization.

Fig. 6. Knowledge portal for organizational creativity support.

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mostly

• the relatively long and complex processes of both software devel-opment and the configuration of software for the intelligent agents,

• the need to monitor and check the accuracy of the individual agentswork, especially in the early stages of their work,

• the need to redesign the work of individual intelligent agents whenchanging the structures of data bases, web pages, etc.,

• unexpected server and ICT infrastructure downtime can destabilizethe work of individual agents.

Summing up, the initial testing of OCSM has proven that suchprototype can support mainly (1) tracking and acquiring of informationfrom various social media (price comparison websites), (2) discoveringnew knowledge (e.g., some patterns, correlations among selectedproducts and consumer opinions), (3) suggesting some ideas andchanges (e.g., in product development/improvement), and (4) dissemi-nating and exchanging new knowledge. OCSM provides the organiza-tion with relevant information (directly from customers, suppliers, andcompetitors) about their opinions, interests, and preferences. Suchinformation may be useful in the evaluation of a product's marketposition and in the determination of the direction of its furtherdevelopment. It may thus be important for the marketing department,promotion, or research and development department in the search fornew and more attractive products.

5. Conclusions

Our study attempts to formulate a new, comprehensive frameworkfor information system design to support organizational creativity. Itconsists of six stages that include (1) problem finding, (2) acquiringinformation resources, (3) information analysis, knowledge discovery,providing some suggestions concerning new ideas, (4) evaluating andselecting discovered knowledge/ideas, (5) communicating newly dis-covered knowledge in an organization and considering whether thenew knowledge should be transformed into innovation, and (6)evolving creative knowledge in an organization and organizationallearning.

The proposed framework incorporates well-established RBV theoryand a multiagent approach. RBV provides the right framework forknowing how to manage information resources and deal with themstrategically. The model advocates that RBV has a natural application tocreativity support. It illustrates that creative processes depend oninformation flows of various sorts and a capacity of new knowledgediscovery. In turn, the multiagent approach presents that particularhuman activities (important in the context of creativity development)may be supported by various intelligent agents.

The proposed framework is based on paradigms and rules presentedby Hevner et al. [82] that refer to canons of design science ininformation systems research [82–84]. They describe how to conductand evaluate a design process of information systems.

The proposed framework was applied and initially tested in a socialmedia environment. As a result, an OCSS called OCSM was built.Customer opinions from the consumer electronics sector were thecenter of the analysis. The system consists of four types of agents.They assist and help perform various activities that concern mainlyacquiring, analyzing, discovering new knowledge and suggesting somerecommendations concerning new and useful ideas. Agents can alsocommunicate with each other, exchange knowledge, and initiatecommunication with human participants.

Our study makes several theoretical contributions to the relevantliterature. First, organizational creativity support is generally anunexplored field of research. Therefore, the current study contributesto the emerging literature on organizational creativity by investigatingthe issue of the design of information system to support organizationalcreativity. Second, the current study is one of the rare studies that

propose a comprehensive and conceptual framework for the design ofan information system to provide organizational creativity support.Third, this study demonstrates how RBV may be used to manageinformation resources and how a multiagent approach may be appliedto support various human activities and tasks. Fourth, the currentfindings report that monitoring, data acquisition, and analyzing anddiscovering new knowledge, for the purpose of organizational creativitysupport may be effectively performed by utilizing intelligent agents.Discovered knowledge as well some suggestion recommended by theOCSS may be helpful for the users in the creation of new ideas. Finally,the current findings provide empirical significance that the design of aninformation system may play an important role in organizationalcreativity support. The conclusions of the current study contribute tothe relevant literature regarding the importance of a comprehensiveview on the design of an information system to provide organizationalcreativity support. These contributions are significant because the voidin the literature regarding these types of conclusions is remarkable. Theobtained findings and outcomes of this study should be useful for anydesigners of information systems as well as managers and organizationswilling to use OCSSs.

There are limitations associated with this research that may narrowthe scope of the findings and point to potential directions for futurestudies. The proposed system OCSM is a kind of initial validation of theproposed framework. It needs further validation and testing. OCSM maybe inadequate to depict a full picture of acquisition, analysis of differentinformation resources, and knowledge discovery for an OCSS.Therefore, future research studies may include design of new intelligentagents (to make more complex analysis) and more information, e.g.,customer opinions on products (depending on the educational level,income, experience, individual cultural values, and loyalty) fromdifferent information resources.

Acknowledgments

This paper has been supported by a grant “Methodology for ComputerSupported Organizational Creativity” from the National Science Centre inPoland, 2013/09B/HS4/00473.

Heartfelt thanks go to the Reviewers. Their professional guidancehelped the authors give final shape to this paper.

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Prof. Celina M. Olszak, Ph.D., D.Sc. is a professor of Management Information Systemsat the University of Economics in Katowice, Poland. She is the chair of the Department ofBusiness Informatics. She is also a DAAD and Swiss Government scholarship holder. Shevisited and took different courses at universities in Europe, the USA, and Australia. She isthe author of 10 books and over 150 academic journal articles. Her research focuses ondecision support systems, knowledge management, management information systems,business intelligence, big data, enterprise resource planning, and IT-based organizationalcreativity. She is a member of the Informing Science Institute in the USA, the PGVNetwork, and the Polish Academy of Sciences.

Tomasz Bartuś, Ph.D., D.Sc. is an assistant professor of Management InformationSystems at the University of Economics in Katowice, Poland. He is the author of over 20journal articles. His research and teaching focuses on business intelligence, customerrelationship management, multi-agent systems, and enterprise resource planning systems.

Paweł Lorek Ph.D., D.Sc. is an assistant professor of Management Information Systemsat the University of Economics in Katowice, Poland. He is the author of over 10 journalarticles. His research and teaching focuses on enterprise resource planning systems, bigdata, artificial intelligence, and neural networks.

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